Checking Distribution for Variables

Plot and fit distributions of variables

Histogram of Velocity of all

Log distribution, 0’s make up 3% of all data

CC.TotalData <- na.omit(TotalData)
head(CC.TotalData)

10^mean(log10(CC.TotalData$v[CC.TotalData$v>0]))

[1] 0.0348809

sd(log10(CC.TotalData$v[CC.TotalData$v>0]))

[1] 0.5467087

Now we’re going to plot each unique krill and their bimodality with a dip test (less than 0.05 is multimodality)

Starting to look at bimodality in the variables by merging the TotalData frame with the “tab” table

plot(tab_AGG$Flow.Rate, tab_AGG$dip.test, xlab = "Flow Rate (cm/s)", ylab = "Dip Test (p.value)")
plot(tab_AGG$Flow.Rate, tab_AGG$skew, xlab = "Flow Rate (cm/s)", ylab = "Skew Test (p.value)")

plot(tab_AGG$Chlorophyll, tab_AGG$dip.test, xlab = "Chlorophyll (mg/L)", ylab = "Dip Test (p.value)")

plot(tab_AGG$Chlorophyll, tab_AGG$skew, xlab = "Chlorophyll (mg/L)", ylab = "Skew Test (p.value)")

Now looking at turning angles

for (i in 1:length(ind)){
plot(CC.TotalData$turn.angle[CC.TotalData$D_V_T==ind[i]], log10(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]]),
            xlab = "Turn Angles",
     ylab = "Velocity",
          main = "") 
}

NA

save.image("~/Post-doc/Data/Total Merged Data File.RData")